|
|
|
|
|
Anastasios Fanariotis, Theofanis Orphanoudakis and Vassilis Fotopoulos
Having as a main objective the exploration of power efficiency of microcontrollers running machine learning models, this manuscript contrasts the performance of two types of state-of-the-art microcontrollers, namely ESP32 with an LX6 core and ESP32-S3 wi...
ver más
|
|
|
|
|
|
|
|
|
Marco Scutari
Bayesian networks (BNs) are a foundational model in machine learning and causal inference. Their graphical structure can handle high-dimensional problems, divide them into a sparse collection of smaller ones, underlies Judea Pearl?s causality, and determ...
ver más
|
|
|
|
|
|
|
|
|
Antonio Chiariello, Gaetano Perillo, Mauro Linari, Raffaele Russo, Salvatore Orlando, Pasquale Vitale and Marika Belardo
This study addresses the crucial role of post-buckling behavior analysis in the structural design of composite aeronautical structures. Traditional engineering practices tend to result in oversized composite components, increasing structural weight. EASA...
ver más
|
|
|
|
|
|
|
|
|
Nan Lao Ywet, Aye Aye Maw, Tuan Anh Nguyen and Jae-Woo Lee
Urban Air Mobility (UAM) emerges as a transformative approach to address urban congestion and pollution, offering efficient and sustainable transportation for people and goods. Central to UAM is the Operational Digital Twin (ODT), which plays a crucial r...
ver más
|
|
|
|
|
|
|
|
|
Raymundo Peña-García, Rodolfo Daniel Velázquez-Sánchez, Cristian Gómez-Daza-Argumedo, Jonathan Omega Escobedo-Alva, Ricardo Tapia-Herrera and Jesús Alberto Meda-Campaña
This research introduces a physics-based identification technique utilizing genetic algorithms. The primary objective is to derive a parametric matrix, denoted as A, describing the time-invariant linear model governing the longitudinal dynamics of an air...
ver más
|
|
|
|